Being a decentralized form of ML, FL performs ML without aggregating user data to a centralized server, thus making it easier to comply with the relevant data privacy regulations [1]}. At each global iteration in FL, the wireless devices first download initialized parameters of a global model from a remote server. Then, the devices train local models using their local available data in parallel. After several local iterations, the devices send their parameters to the server, and the global model is updated using the uploaded local parameters. The learning process is repeated until the global model achieves a preset accuracy. Fig. REF  depicts the mechanism of a typical FL paradigm.
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